Wood,, T. A., & Laughton, S. (2023, June), Latest Improvements in Metacognitive-Informed, Dual-Submission Homework Methods  Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--44018

\bibitem{asee_peer_44018}
  Wood,, T. A., \& Laughton, S. (2023, June), \emph{Latest Improvements in Metacognitive-Informed, Dual-Submission Homework Methods}
  Paper presented at 2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland.
  10.18260/1-2--44018

Timothy Aaron Wood, P.E. and Stephanie Laughton.  "Latest Improvements in Metacognitive-Informed, Dual-Submission Homework Methods".  2023 ASEE Annual Conference & Exposition, Baltimore , Maryland, 2023, June.  ASEE Conferences, 2023.  https://peer.asee.org/44018 Internet.  07 Jun, 2024

\bibitem{asee_peer_44018}
  Timothy Aaron Wood, P.E. and Stephanie Laughton.
  "Latest Improvements in Metacognitive-Informed, Dual-Submission Homework Methods".
  \emph{2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland, 2023, June}.
  ASEE Conferences, 2023.
  https://peer.asee.org/44018 Internet.  07 Jun, 2024

@INPROCEEDINGS{asee_peer_44018
author = "Timothy Aaron Wood, P.E. and Stephanie Laughton"
title = "Latest Improvements in Metacognitive-Informed, Dual-Submission Homework Methods"
booktitle = "2023 ASEE Annual Conference \& Exposition"
year = "2023"
month = "June"
address = "Baltimore , Maryland"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/44018}
number = {10.18260/1-2--44018}
}

TY  - CPAPER
AB  - Over the last decade, in response to increasing frequency of academic misconduct, engineering education has explored different methods for providing practice, feedback, and scaffolded learning experiences through homework. Though many innovative tools have been developed to provide immediate feedback to students, ultimately students need practice and critical thinking about their practice to engage in long-term effective learning and content mastery. This synthesis of the growing body of literature around metacognitive, self-graded, and dual-submission homework methods presents best practices for helping students get the most out of homework assignments and develop increasing competence in self-directed learning. In sophomore-level courses, the dual submission with reflection implementation asks students to submit initial attempts at the homework problems to earn credit for completion. Then the students use an instructor-provided solution to check and correct their work, also for completion-based credit. Alongside the dual submission homework problems, students may develop their metacognitive skills by completing short reflections on their learning. To increase student responsibility for content mastery in a junior-level course, initial submission with immediate assessment implementation asks students to check their own work without granting credit for corrections. In a senior-level course, an auto-graded with rework submission implementation gives students an opportunity to earn 100% credit regardless of initial accuracy, but students must develop reworked solutions from an instructor provided numerical answer, not a comprehensive solution. As more instructors employ metacognitive-informed dual-submission homework methods, instructors can adjust their implementations to scaffold increasing responsibility for self-directed, life-long learning and engineering accuracy. At every stage, students should experience learning, problem solving and evaluating their own work like a practicing engineer.
AU  - Timothy Aaron Wood, P.E.
AU  - Stephanie Laughton
CY  - Baltimore , Maryland
DA  - 2023/06/25
PB  - ASEE Conferences
TI  - Latest Improvements in Metacognitive-Informed, Dual-Submission Homework Methods
UR  - https://peer.asee.org/44018
DO  - 10.18260/1-2--44018
ER  -